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Initializer that generates a delta orthogonal kernel for ConvNets.

Inherits From: Initializer

The shape of the tensor must have length 3, 4 or 5. The number of input filters must not exceed the number of output filters. The center pixels of the tensor form an orthogonal matrix. Other pixels are set to be zero. See algorithm 2 in (Xiao et al., 2018).

gain Multiplicative factor to apply to the orthogonal matrix. Default is 1. The 2-norm of an input is multiplied by a factor of gain after applying this convolution.
seed A Python integer. Used to create random seeds. See tf.compat.v1.set_random_seed for behavior.
dtype Default data type, used if no dtype argument is provided when calling the initializer. Only floating point types are supported.


Xiao et al., 2018 (pdf)



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Instantiates an initializer from a configuration dictionary.


initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)

config A Python dictionary. It will typically be the output of get_config.

An Initializer instance.


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Returns the configuration of the initializer as a JSON-serializable dict.

A JSON-serializable Python dict.


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Returns a tensor object initialized as specified by the initializer.

shape Shape of the tensor.
dtype Optional dtype of the tensor. If not provided use the initializer dtype.
partition_info Optional information about the possible partitioning of a tensor.